Methods and apparatus to determine synthetic respondent level data

ABSTRACT

Methods, apparatus, systems, and articles of manufacture are disclosed to generate synthetic respondent level data. An example apparatus includes: a comparator to compare a target rating to a computed rating to determine a comparison result, the computed rating determined based on a seed panel, the seed panel including monitored panelists associated with return path data; a seed panelist data adjuster to adjust the seed panel based on the comparison result to reduce an error between the target rating and the computed rating; and a household data adjuster to add tuning without viewing data to households of the adjusted seed panel, the tuning without viewing data for a first one of the households to represent monitored data corresponding to a media presentation device of the first one of the households being on while a media output device in communication with the media presentation device is off.

RELATED APPLICATION

This patent arises from a continuation of U.S. patent application Ser.No. 15/445,557, which is titled “METHODS AND APPARATUS TO DETERMINESYNTHETIC RESPONDENT LEVEL DATA,” and which was filed on Feb. 28, 2017.Priority to U.S. patent application Ser. No. 15/445,557 is claimed. U.S.patent application Ser. No. 15/445,557 is incorporated herein byreference in its entirety.

FIELD OF THE DISCLOSURE

This disclosure relates generally to media audience measurement, and,more particularly, to methods and apparatus to determine syntheticrespondent level data.

BACKGROUND

Determining a size and demographic of an audience of a mediapresentation helps media providers and distributors schedule programmingand determine a price for advertising presented during the programming.In addition, accurate estimates of audience demographics enableadvertisers to target advertisements to certain types and sizes ofaudiences. To collect these demographics, an audience measurement entityenlists a plurality of media consumers (often called panelists) tocooperate in an audience measurement study (often called a panel) for apredefined length of time. In some examples, the audience measuremententity obtains (e.g., directly, or indirectly via a service provider)return path data from media presentation devices (e.g., set-top boxes)that identifies tuning data from the media presentation device. In suchexamples, the audience measurement entity models and/or assigns viewersbased on the return path data. The media consumption habits anddemographic data associated with these enlisted media consumers arecollected and used to statistically determine the size and demographicsof the entire audience of the media presentation. In some examples, thiscollected data (e.g., data collected via measurement devices) may besupplemented with survey information, for example, recorded manually bythe presentation audience members.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of an example environment in which return pathdata and meter data are collected from media presentation locations andare analyzed by an example audience measurement entity to generate anexample seed panel and generate example synthetic respondent level databased on the example seed panel.

FIG. 2 is a block diagram of an example implementation of an exampleseed panel generator of FIG. 1.

FIG. 3 is a block diagram of an example implementation of an exampleseed panel optimizer of FIG. 1.

FIGS. 4-6 are flowcharts illustrating example machine readableinstructions that may be executed to implement the example seed panelgenerator of FIGS. 1 and/or 2.

FIGS. 7-11 are flowcharts illustrating example machine readableinstructions that may be executed to implement the example seed paneloptimizer of FIGS. 1 and/or 3.

FIG. 12 is a block diagram of an example processing system structured toexecute the example machine readable instructions of FIGS. 4-6 toimplement the example seed panel generator of FIGS. 1 and/or 2.

FIG. 13 is a block diagram of an example processing system structured toexecute the example machine readable instructions of FIGS. 7-11 toimplement the example seed panel generator of FIGS. 1 and/or 3.

DETAILED DESCRIPTION

Audience measurement entities seek to understand the composition andsize of audiences of media, such as television programming. Suchinformation allows audience measurement entity researchers to, forexample, report advertising delivery and/or targeting statistics toadvertisers that target their media (e.g., advertisements) to particularaudiences. Additionally, such information helps to establish advertisingprices commensurate with audience exposure and demographic makeup(referred to herein collectively as “audience configuration”). One wayto gather media presentation information is to gather media presentationinformation from media output devices (e.g., gathering televisionpresentation data from a set-top box (STB) connected to a television).As used herein, media presentation includes media output by a mediadevice regardless of whether or not an audience member is present (e.g.,media output by a media output device at which no audience is present,media exposure to an audience member(s), etc.).

A media presentation device (e.g., STB) provided by a service provider(e.g., a cable television service provider, a satellite televisionservice provider, an over the top service provider, a music serviceprovider, a movie service provider, a streaming media provider, etc.) orpurchased by a consumer may contain processing capabilities to monitor,store, and transmit tuning data (e.g., which television channels aretuned by the media presentation device at a particular time) to anaudience measurement entity (e.g., The Nielsen Company (US), LLC.) toanalyze media presentation activity. Data transmitted from a mediapresentation device back to a service provider providing the media(which may then aggregate and provide the return path data to anaudience measurement entity) is herein referred to as return path data.Return path data includes tuning data. Tuning data is based on datareceived from the media presentation device while the media presentationdevice is on (e.g., powered on, switched on, and/or tuned to a mediachannel, streaming, etc.). Although return path data includes tuningdata, return path data may not include data (e.g., demographic data)related to the user viewing the media corresponding to the mediapresentation device. Accordingly, return path data may not be associatedwith particular viewers, demographics, locations, etc.

To determine aspects of media presentation data (e.g., which householdmember is currently consuming a particular media and the demographics ofthat household member), market researchers may perform audiencemeasurement by enlisting a subset of the media consumers as panelists.Panelists or monitored panelists are audience members (e.g., householdmembers, users, panelists, etc.) enlisted to be monitored, who divulgeand/or otherwise share their media activity and/or demographic data tofacilitate a market research study. An audience measurement entitytypically monitors media presentation activity (e.g., viewing,listening, etc.) of the monitored panelists via audience measurementsystem(s), such as a metering device(s) and/or a local people meter(LPM). Audience measurement typically includes determining the identityof the media being presented on a media output device (e.g., atelevision, a radio, a computer, etc.), determining data related to themedia (e.g., presentation duration data, timestamps, channel data,etc.), determining demographic information of an audience, and/ordetermining which members of a household are associated with (e.g., havebeen exposed to) a media presentation. For example, an LPM incommunication with an audience measurement entity communicates audiencemeasurement (e.g., metering) data to the audience measurement entity. Asused herein, the phrase “in communication,” including variances thereof,encompasses direct communication and/or indirect communication throughone or more intermediary components and does not require direct physical(e.g., wired) communication and/or constant communication, but ratheradditionally includes selective communication at periodic or aperiodicintervals, as well as one-time events.

In some examples, metering data (e.g., including media presentationdata) collected by an LPM or other meter is stored in a memory andtransmitted via a network, such as the Internet, to a datastore managedby the audience measurement entity. Typically, such metering data iscombined with additional metering data collected from a plurality ofLPMs monitoring a plurality of panelist households. The metering datamay include, but are not limited to, a number of minutes a householdmedia presentation device was tuned to a particular channel, a number ofminutes a household media presentation device was used (e.g., consumed)by a household panelist member and/or a visitor (e.g., a presentationsession), demographics of the audience (which may be statisticallyprojected based on the panelist data), information indicative of whenthe media presentation device is on or off, and/or informationindicative of interactions with the media presentation device (e.g.,channel changes, station changes, volume changes, etc.), etc. As usedherein, a channel may be a tuned frequency, selected stream, an addressfor media (e.g., a network address), and/or any other identifier for asource and/or carrier of media.

Return path data provides valuable media exposure data, including mediaexposure data in locations where no panel data is available. However,return path data typically contains tuning data in the aggregate.Accordingly, return path data usually does not include respondent leveldata such as, but not limited to, detailed data relating to audiencedemographics and/or viewing data broken up into margins (e.g., quarterhours). Examples disclosed herein alleviate the lack of respondent leveldata in return path data by leveraging the respondent level dataobtained from a panel of monitored panelists. Using examples disclosedherein, synthetic respondent level data corresponding to a group ofsynthetic, or virtual, panelists may be generated to correspond to thereturn path data, thereby increasing the value of return path data to acustomer (e.g., of an advertising company).

Examples disclosed herein process the collected and/or aggregatedmetering data for markets where a panel is maintained and collect and/oraggregate return path data for markets where a panel is not maintainedto generate a seed panel. A seed panel is a synthetic panel includingmonitored panelists and non-panelist selected to correspond to returnpath data homes (e.g., in-market return path data) and regional panelhomes (e.g., over the air only panelists) and used as the basis forgeneration of synthetic respondent level data (e.g., representative of agroup synthetic/virtual panelists) based on a similarity to the segmentof the market that is not covered by return path data. These monitoredpanelists are selected from a panel (e.g., a national panel of meteredusers) based on a regional proximity to a designated market area, asimilarity between demographics of the monitored panelist anddemographics of the return path data audience location, household mediacharacteristics (e.g., how the households receive television signals(cable, satellite, over-the-air radio, etc.)), a similarity betweenmedia consumption of the monitored panelists and the return path dataaudience, etc. As used herein, a return path data audience is viewerassigned return path data associated with a population (e.g., a universeor users) and/or location. As used herein, a seed panelist is amonitored panelist that has been selected to be included in a seedpanel. As used herein, synthetic respondent level data or respondentlevel data is processed viewing data at the level of individualrespondents. Synthetic respondent level data may include complete timerecords (e.g., at the quarter hour level, hour level, etc.) across eachbroadcasting day of all viewing session by every family member and gueston all metered media output devices in a home including the demographicdata. As used herein, designated market area is a geographical area thatdefines a media market where synthetic respondent level data isproduced.

Once a seed panel has been generated, examples disclosed herein adjustthe seed panel to satisfy target ratings and/or target reach. As usedherein, a rating is an average percentage of a population exposed tomedia across a set time interval. As used herein, reach is a cumulativepercentage or total of a population that has been counted as a viewer ofmedia at least once during a specified time interval (e.g., daily,weekly, monthly, etc.). Examples disclosed herein adjust the seed panelby adjusting weights of seed panelists corresponding to the targetrating and/or reach (such as the target rating and/or reach representedby the aggregate return path data) until the target rating and/or reachis satisfied. For example, if the target rating (e.g., corresponding toa rating reflected in the aggregate return path data) is 25% of menexposed to a first program during a first duration of time and 30% ofthe generated seed panel men were exposed to the first program duringthe first duration of time, examples disclosed herein adjust the seedpanel to reduce the current rating (e.g., 30%) to a rating closer to thetarget rating (e.g., 25%). Examples disclosed herein generate an outputfile including synthetic respondent level data for the adjusted seedpanelists corresponding to the target rating. Using examples disclosedherein, consistent respondent level data is generated that satisfyvarious targets, thereby providing more accurate universe estimations.

FIG. 1 is a block diagram of an environment in which example return pathdata 100 and example meter data 102 are collected to generate syntheticrespondent level data based on a generated seed panel. FIG. 1 includesthe example return path data 100, the example meter data 102, an examplemedia provider 104, an example media presentation device 106, examplemedia output devices 108, 110, an example local people meter (LPM) 112,and an example audience measurement entity (AME) 114. The exampleaudience measurement entity 114 includes an example modeler 116, anexample return path data (RPD) audience storage 118, an example panelistdata storage 120, an example seed panel generator 122, an examplestation data storage 124, an example seed panel storage 126, an exampleseed panel optimizer 128, and an example output file 130.

The example media provider 104 of FIG. 1 is a service provider (e.g.,cable media service provider, a radio frequency (RF) media provider, asatellite media service provider, etc.) that presents media to anaudience member via the example media presentation device 106. The mediaprovided by the example media provider 104 is transmitted (e.g., via awired or wireless network connection) to the media presentation device106. The media presentation device 106 is connected, via a wired orwireless connection, to the example media output device 108 to outputthe media to an audience member. The media output device 108 is a devicecapable of outputting the received media. For example, the media outputdevice 108 may be a television, a radio, speakers, a projector, acomputer, a computing device, a tablet, a mobile device, and/or anyother device capable of outputting media.

When the example media presentation device 106 of FIG. 1 is on, themedia presentation device 106 receives media corresponding to a station,program, website, etc. based on the tuning of the example mediapresentation device 106. For example, the media presentation device 106may be a set-top box. Additionally or alternatively, the example mediapresentation device 106 may be an over the top device, a video gameconsole, a digital video recorder (DVR), a digital versatile disc (DVD)player, a receiver, a router, a server, a computer, a mobile device, asmart television, and/or any device that receives media from a serviceprovider. In some examples, the media presentation device 106 mayimplement a DVR and/or DVD player. In some examples, the example mediapresentation device 106 includes a unique serial number that, whenassociated with subscriber information, allows an audience measuremententity, a marketing entity, and/or any other entity to ascertainspecific subscriber behavior information.

By way of example, the example media presentation device 106 may betuned to channel 5. In such an example, the media presentation device106 outputs media (from the example media provider 104) corresponding tothe tuned channel 5. The media presentation device 106 may gather tuningdata corresponding to which channels, stations, websites, etc. that theexample media presentation device 106 was tuned. The example mediapresentation device 106 generates and transmits the example return pathdata 100 to the example media provider 104. The example return path data100 includes the tuning data and/or data corresponding to the examplemedia provider 104 (e.g., data in the aggregate). Although theillustrated example of FIG. 1 includes the example media provider 104receiving the example return path data 100 from one media presentationdevice (e.g., the example media presentation device 106), at onelocation, corresponding to one media provider (e.g., the example mediaprovider 104), the example media provider 104 may receive return pathdata 100 from any number or type(s) of media presentation devices, atany number of locations. The media provider 104 transmits the collectedreturn path data 100 to the example audience measurement entity 114.Additionally or alternatively, the audience measurement entity 114 maybe hosted by any other entity or may be co-hosted by anotherentity(ies). For example, the example return path data 100 may becollected from the example media presentation devices 106 by a mediaprovider (e.g., a cable television provider, a satellite televisionprovider, etc.) and the example meter data 102 may be collected from anLPM (e.g., such as the example LPM 112) by the example audiencemeasurement entity 114 cooperating with the media provider to gainaccess to the tuning data. The example audience measurement entity 114includes the example return path data audience storage 118 (e.g., adatabase) and the example panelist data storage 120 (e.g., a database).

The example media output device 110 of FIG. 1 is a device capable ofoutputting the received media. For example, the media output device 110may be a television, a radio, speakers, a projector, a computer, acomputing device, a tablet, a mobile device, and/or any other devicecapable of outputting media. In some examples, the media output device110 receives media over-the-air. In this manner, the media output device110 receives media via an antenna and does not correspond to a mediaprovider (e.g., including the example media provider 104). In theillustrated example of FIG. 1, the media output device 110 correspondsto one or more monitored panelists. The example LPM 112 monitors thepanelists exposure to media output by the example media output device110. For example, the example LPM 112 is in communication with theexample media output device 110 to collect and/or capture signalsemitted externally by the media output device 110. The LPM 112 may becoupled with the media output device 110 via wired and/or wirelessconnection. The example LPM 112 may be implemented in connection withadditional and/or alternative types of media presentation devices, suchas, for example, a radio, a computer monitor, a video game console,and/or any other device capable to present media to a user. The LPM 112may be a portable people meter, a cell phone, a computing device, asensor, and/or any other device capable of metering (e.g., monitoring)user exposure to media. In some examples, a media presentation locationmay include a plurality of LPMs 112. In such examples, the plurality ofthe LPMs 112 may be used to monitor media exposure for multiple usersand/or media output devices 110. Additionally, the example panelist datastorage 120 receives and stores the example meter data 102 from theexample LPM 112.

In some examples, the example LPM 112 of FIG. 1 includes a set ofbuttons assigned to audience members to determine which of the audiencemembers is watching the example media output device 110. The LPM 112 mayperiodically prompt the audience members via a set of LEDs, a displayscreen, and/or an audible tone, to indicate that the audience member ispresent at a first media presentation location by pressing an assignedbutton. In some examples, to decrease the number of prompts and, thus,the number of intrusions imposed upon the media consumption experienceof the audience members, the LPM 112 prompts only when unidentifiedaudience members are located at the first media presentation locationand/or only after the LPM 112 detects a channel change and/or a changein state of the media output device 110. In other examples, the LPM 112may include at least one sensor (e.g., a camera, 3-dimensional sensor,etc.) and/or be communicatively coupled to at least one sensor thatdetects a presence of the user in a first example media presentationlocation. The example LPM 112 transmits the example meter data 102 to amedia researcher and/or a marketing entity. The example meter data 102includes the media presentation data (e.g., data related to mediapresented while the media output device 110 is on and a user ispresent). The example meter data 102 may further include a householdidentification, a tuner key, a presentation start time, a presentationend time, a channel key, etc. Although the illustrated exampleillustrates the example audience measurement entity 114 collecting theexample meter data 102 from one LPM 112 at one location, the exampleaudience measurement entity 114 may collect meter data from any numberor type of meters at any number of locations.

The example return path data 100 of FIG. 1 from the example mediapresentation device 106 and/or the example meter data 102 from theexample LPM 112 is transmitted to the example audience measuremententity 114 via a network. The network may be implemented using any typeof public or private network, such as, but not limited to, the Internet,a telephone network, a local area network (LAN), a cable network, and/ora wireless network. To enable communication via the network, the examplemedia presentation device 106 includes a communication interface thatenables a connection to an Ethernet, a digital subscriber line (DSL), atelephone line, a coaxial cable, or any wireless connection, etc.

The example modeler 116 of the example AME 114 of FIG. 1 collects theexample return path data 100 corresponding to the example mediapresentation device(s) 106. As described above, the example return pathdata 100 includes tuning data of the example media presentation device106. However, the example return path data 100 may not include specificdata identifying any information relating to the audience of the examplemedia output device 108. The example modeler 116 models such audienceinformation. For example, the modeler 116 may assign and/or modelvirtual users to augment the example return path data 100, therebygenerating audience (e.g., viewer or listener) assigned return pathdata. The example modeler 116 outputs the audience assigned return pathdata to the example return path data audience storage 118.

The example seed panel generator 122 of FIG. 1 gathers (A) the audienceassigned return path data from the example return path data audiencestorage 118, (B) the example meter data 102 from the example panelistdata storage 120, (C) and station data from the example station datastorage 124 to generate a seed panel. As explained above, a seed panelis a panel including synthetic respondent level data from monitoredpanelists corresponding to the LPM(s) 112, which are selected based on areturn path data audience and/or homes and regional panel audienceand/or homes that are not covered by return path data. The seedpanelists are selected to represent the entire market. The example seedpanel generator 122 assigns geography and income data to the personsand/or homes corresponding to the audience assigned return path data andthe meter data 102. The example seed panel generator 122 initiates theseed panel by selecting monitored panelists to be representative of theviewer/geography/income assigned return path data audience. For example,a monitored panelist may be selected based on a similarity between (A)the location of the monitored panelist and the location of a return pathdata audience member, (B) demographics corresponding to the location ofthe return path data audience member and the demographics of themonitored panelist, (C) media viewing characteristics of the return pathdata audience and the monitored panelist, etc.

The example station data storage 124 stores data related to stationreceivability by county. The example seed panel generator 122 uses thestation data to calculate the station receivability for over the airhomes, as further described in conjunction with FIG. 5. In someexamples, the seed panel generator 122 filters the gathered seedpanelists to collect attributes of interest at the person level and/orthe household level. Attributes of interest at the person level mayinclude age, gender, ethnicity, nationality, race, etc. and attributesat the household level may include head of household data, cable data,single set data, ADS data, county data, metro data, income, zip code,number of televisions, pay service data, etc. The example seed panelgenerator 122 weights the seed panelists according to the universeestimate(s) of the designated market area. The universe estimate is anestimate of the total number of users in a universe of users (e.g.,total number of television viewers). In some examples, the universeestimate is broken down at the demographic level. In some examples, whenout-of-tab seed panelists exist, the example seed panel generator 122donates viewing based on a donor pool of seed panelists and/or monitoredpanelists of similar demographics. A seed panelist is out-of-tab when,for example, the panelist's LPM 112 is off, broken, and/or otherwisefaulty. The viewing donation is further described below in conjunctionwith FIG. 6. Additionally, the example seed panel generator 122 mayreplicate and/or down-sample seed panelists according to a replicationparameter to increase and/or decrease the degrees of freedom of thefinal seed panel. The example seed panel generator 122 replicates seedpanelists by splitting seed panelists into two or more seed panelistswhose weight is distributed among the two representative seed panelists.The example seed panel generator 122 down-samples the seed panelists bycombining demographically similar seed panelists by combining the weightof the two or more seed panelists. The example seed panel generator 122stores the final seed panel in the example seed panel storage 126.

The example seed panel optimizer 128 of FIG. 1 adjusts the seed panelgenerated by the example seed panel generator 122 to satisfy targetratings and/or target household ratings based on constraints. Suchconstraints may include audience constraints, including quarter hourconstraints, daypart constraints, daily constraints, weekly constraints,monthly constraints, etc. Such constraints may also include reachconstraints, including daypart reach constraints, daily reachconstraints, weekly reach constraints, monthly reach constraints, etc.In some examples, the seed panel optimizer 128 applies a discreteoptimization greedy search to adjust the panels to satisfy the targetratings and/or target household ratings based on the constraints.Additionally, the example seed panel optimizer 128 may add tuningwithout viewing data to households of the example seed panel. Tuningwithout viewing occurs when the example media presentation device 106 ison and the example media output device 108 is off, thereby affecting theaccuracy of the example return path data 100 (e.g., the example returnpath data 100 identifies a program as being watched even though theexample media output device 108 is off). The example seed paneloptimizer 128 adjust the seed panel to account for tuning withoutviewing.

The example output file 130 of FIG. 1 includes the adjusted seed panelbased on the target ratings, target household ratings, and otherconstraints. The example output file 130 may additionally include anyother data related to the seed panel. In some examples, the output file130 includes synthetic respondent level data (e.g., detailed demographicdata of the adjusted seed panel), synthetic respondent level attributes,quarter hour ratings calculated from the synthetic respondent leveldata, daypart ratings calculated from the synthetic respondent leveldata, daypart reach calculated from the synthetic respondent level data,etc. Such data may be used to generate a report and/or may be furtherprocessed by a device (e.g., to estimate data related to the universe ofusers).

FIG. 2 is a block diagram of an example implementation of the exampleseed panel generator 122 of FIG. 1 to generate a seed panel and outputthe seed panel to the example seed panel storage 126. The example seedpanel generator 122 includes an example interface(s) 200, an exampledata assigner 202, an example station translator 204, an exampleattributes filter 206, an example weighter 208, an example viewingdonator 210, and an example seed panelist replicator 212.

The example interface(s) 200 of FIG. 2 receives audience assigned returnpath data from the example return path data audience storage 118, meterdata from the example panelist data storage 120, and/or station datafrom the example station data storage 124. Additionally, the exampleinterface(s) 200 outputs the generated seed panel to the example seedpanel storage 126. The example interface(s) 200 may be one interfacecapable of receiving and transmitting data to the example storages 118,120, 124, 126 or may be multiple interfaces to interface with each,and/or any combination of, the example storages 118, 120, 124, 126. Insome examples, the interface(s) 200 receive penalty coefficients from auser. The penalty coefficients are used to upweight and/or downweightthe effect of certain constraints on each panelist's final penaltyscore. In this manner, a user can decide whether it is more important toconverge on household targets as appose to demographic targets bysetting the penalty coefficients.

The example data assigner 202 of FIG. 2 assigns geography data (e.g.,county data) and income data to the audience assigned return path dataaudience and the monitored panelists corresponding to the example meterdata 102. The example data assigner 202 uses designated marked areadefinitions by county, universe estimates for each county (e.g., numberof homes, demographic composition of the homes, etc.), and/ordemographics of seed panel homes to assign the geography data.Additionally or alternatively, the example data assigner 202 may assigngeography data based on any grouping of land (e.g., city, state, etc.)The example data assigner 202 determines the geography data that will beassigned to the monitored panelists and the example audience assignedreturn path data audience by reducing error assigned to seed panel homesto counties in the designated market area based on probabilities ofbelonging to each country. In some examples, the data assigner 202determines the geography data based on constraints, such as countycapacity. The example data assigner 202 calculates the probability of amonitored panelist home belonging to a particular county based oncomparison of seed panel demographics, demographics of the counties,universe estimates of the counties, and/or custom tabulations. To assigngeography to an audience assigned return path data monitored panelist,the example data assigner 202 may generate the seed panel homes assupply nodes (e.g., representing items we want to assign or allocate),generate the counties as demand nodes (e.g., representing items we wantsupplies allocated to), and generate permissible assignments between thesupply and demand nodes. The example data assigner 202 determines eachcost for the permissible assignments and selects the geographyassignment corresponding with the lowest cost. The example data assigner202 of FIG. 2 assigns the income data based on the assigned county,postal codes, population distribution, income data, universe estimates,custom demographic data. In some examples, the data assigner 202processes various data to assign income using a linear interpolation,full kernel estimate cumulative density function, and/or any othermathematical modelling. In some examples, the example data assigner 202initiates the seed panel by selecting monitored panelists to representthe return path data audience based on a regional proximity to adesignated market area, a similarity between demographics of themonitored panelist and demographics of the return path data audiencelocation, household media characteristics (e.g., how the householdsreceive television signals (cable, satellite, over-the-air radio,etc.)), a similarity between media consumption of the monitoredpanelists and the return path data audience, etc.

The example station translator 204 of FIG. 2 determines a stationreceivability list for the list of stations that are viewable by aparticular audience member. The example return path data 100 may includethe station receivability list and/or data corresponding to a stationreceivability list. However, meter data 102 may not include stationreceivability lists. To determine a station receivability list for theexample media output device 110 of FIG. 1 (e.g., corresponding to overthe air media), the example station translator 204 receives station datafrom the example station data storage 124 via the example interface(s)200. The station data includes a list of viewable stations within acounty. In some examples, the example station translator 204 translatesviewing from the meter data 102 into a receivable station based on thestation data. The translation of viewing into a receivable station isfurther described below in conjunction with FIG. 5.

The example attributes filter 206 of FIG. 2 filters the selected seedpanelists to carry over certain attributes (e.g., person and/orhousehold attributes) without any additional modeling. As describedabove, such attributes at the person level may include age, gender,ethnicity, nationality, race, etc. and such attributes at the householdlevel may include head of household data, cable data, single set data,ADS data, county data, metro data, income, zip code, number oftelevisions, pay service data, etc.

The example weighter 208 of FIG. 2 weights the seed panelists accordingto the universe estimates. The seed panel may not accurately representthe total universe. Accordingly, the example weighter 208 weights theseed panelists so that the seed panel better represents the universeestimates. In this manner, the seed panelists are a statistically validrepresentation of the total universe of users.

The example viewing donator 210 of FIG. 2 donates viewing for out-of-tabseed panelists. A seed panelist is out-of-tab when, for example, thepanelist's LPM 112 is off, broken, and/or otherwise faulty.Additionally, a seed panelist may be out-of-tab when the example returnpath data 100 is faulty or not being transmitted to the example AME 114.In such examples, the seed panelist may be watching the example mediaoutput device 108, 110, but not being credited for the viewing.Accordingly, the example viewing donator 210 determines when a seedpanelist is out-of-tab and donates viewing data to represent the seedpanelist until the seed panelist is back in-tab. Each seed panelist mayhave a corresponding donor pool of seed panelists and/or monitoredpanelists with similar demographics. When the example viewing donator210 determines that the seed panelist is out-of-tab, the example viewingdonator 210 selects a donor from the donor pool and uses the viewingdata of the donor to represent the out-of-tab panelist. An example ofdonating viewing is further described below in conjunction with FIG. 6.

The example seed panelist replicator 212 of FIG. 2 replicates and/ordown-samples the seed panel prior to storing in the example seed panelstorage 126. The example seed panelist replicator 212 replicates and/ordown-samples to increase or decrease the degrees of freedom of the seedpanel. In this manner, the seed panel can be more easily adjusted tosatisfy target ratings and/or household target ratings. The example seedpanelist replicator 212 replicates a seed panelist by splitting the seedpanelist into two or more seed panelists of equal weight. For example,if a first seed panelist has a weight of 10, the example seed panelistreplicator 212 may split the seed panelist into two seed panelists ofweight 5. In this manner, the seed panel still represent the totaluniverse, but presents a more granular data set that can be easilyadjusted to satisfy various constraints. Additionally, demographicallysimilar seed panelists may be combined to down-sample the seed panel.

FIG. 3 is block diagram of an example implementation of the example seedpanel optimizer 128 of FIG. 1 to generate the example output file 130based on adjusting the seed panel to satisfy target ratings and/orhousehold ratings. The example seed panel optimizer 128 includes anexample interface(s) 300, an example constraint determiner 302, anexample ratings/reach comparer 304, an example penalty determiner 306,an example seed panelist data adjuster 308, an example household dataadjuster 310, and the example file generator 312.

The example interface(s) 300 of FIG. 3 receive the seed panel from theexample seed panel storage 126 and receive targets. The targets aretarget audience numbers (e.g., total count or percentage), householdnumbers, and/or reach numbers to be satisfied by the syntheticrespondent level data. For example, a ratings target may include apercentage of a demographic group that viewed a program, channel, etc.within a quarter hour, day-part, day, etc. The targets may be based onknown data from the universe, surveys, modeling, and/or other datasources. Because the seed panel may not correspond to the targets, theexample seed panel optimizer 128 adjusts the panel to satisfy thetargets, thereby outputting the example output file 130 corresponding tosynthetic level respondent level data of the adjusted seed panel.

The example constraint determiner 302 of FIG. 3 determines theconstraints for adjusting the seed panelists. For example, some seedpanelists may not be adjusted. The example constraint determinerdetermines 302 which seed panelists can and which seed panelists cannotbe adjusted and the parameters corresponding to the adjustment (e.g.,how much can a seed panelist be adjusted). The example constraintdeterminer 302 further determines audience constraints corresponding tothe seed panel. In some examples, the constraint determiner 302 receivesaudience constraints form a regionally calibrated quarter hour ratingsfile. In such examples, to generate quarter hour rating constraints, theexample constraint determiner 302 takes percent rating directly from thefile and converts to total quarter hours by multiplying thecorresponding Universe Estimate. To generate daypart ratingsconstraints, the example constraint determiner 302 sums total quarterhours across the appropriate time window and aggregates up to differentperiods (e.g., daily, weekly, monthly, etc.). In some examples theconstraint determiner 302 sums the total quarter hours viewed by allstations in a station mapping file to ensure a reasonable number of seedpanelists and households are tuning at any given time. Additionally, theexample constraint determiner 302 determines if consistency existsbetween the household audience constraints and the audience constraintsfor each demographic by calculating a maximum ratio. The maximum ratiois the sum of a person's weights for a given demographic divided by thesum of weights corresponding to households containing a member of thedemographic. If the ratio of the person's viewership constraint for thedemographic to the person's household viewership constraint exceeds themaximum ratio in any demographic by any quarter hour and/or station, theexample constraint determiner 302 sets the person's viewershipconstraint to the household viewership constraint multiplied by themaximum ratio of the demographic.

Additionally, the example constraint determiner 302 of FIG. 3 determinesreach constraints from the seed panel data. The example constraintdeterminer 302 calculates the reach constraints at the day part level.In a manner similar to the audience constraints, the example constraintdeterminer 302 calculates reach across periods (e.g., daily, weekly,monthly) using the reach daypart constraint. Additionally, the exampleconstraint determiner 302 may calculate reach across all stations of astation mapping file to ensure a reasonable number of unique seedpanelists and households that are tuning at any given time.

The example ratings/reach comparer 304 of FIG. 3 compares the targetratings to the seed panel ratings, the target household ratings to theseed panel household rating, the target reach to the seed panel reach,and/or the target household reach to the seed panel household reach.Additionally, the example ratings/reach comparer 304 determines if thecomparisons satisfy one or more thresholds (e.g., viewing threshold(s),error threshold(s), etc.).

The example penalty determiner 306 of FIG. 3 determines penaltiescorresponding to a seed panelist and/or a seed panelist household. Thepenalty score of a seed panelist and/or household is a weighted sum thatquantifies the relative error observed if the viewing of the seedpanelist and/or household is modified. The formula(s) for calculatingpenalty (e.g., persons rating penalty, household rating penalty, personsreach penalty, and/or household reach penalty) is based on theviewership error (e.g., current rating−target rating) and is shown belowin Table 1.

TABLE 1 Penalty Formulas NAME FORMULA Person-level Ratings If cell needsto be increased: 1. Quarter Hour error = target − current 2. Daypart Ifcell needs to be decreased: 1. All-station error = current − target Iferror == 0: penalty = 1 − 1/pwgt If error <> 0: penalty = abs(1 −pwgt/error) Where: target is the target rating current is the currentrating observed in synthetic panel pwgt is the person weight of thegiven panelist. Household-level Ratings If cell needs to beincreased: 1. Quarter Hour error = target − current 2. Daypart hct =hct + 1 3. All station If cell needs to be decreased: Household-levelReach error = current − target 1. Daypart hct = hct If error == 0: score= (1 − 1/hwgt) + 1/hct If error > 0: score = (1 − hwgt/error) + (1 − 1/hct) If error < 0: score = (1 − hwgt/error) + 1/ hct If score > 2:penalty = 1 Else: penalty = score/2 Where: target is the targethousehold ratings current is the current household rating observed insynthetic panel hwgt is the household weight of the given panelist'sassigned household hct is the total number of times anyone in thispanelist's household viewed this station(s) during this time periodPerson-level Reach If cell needs to be increased: Daypart error = target− current pct = pct + 1 If cell needs to be decreased: error = current −target pct = pct If error == 0: score = (1 − 1/pwgt) + 1/pct If error >0: score = (1 − pwgt/error) + (1 − 1/ pct) If error < 0: score = (1 −pwgt/error) + 1/pct If score > 2: penalty = 1 Else: penalty = score/2Where: target is the target person reach current is the current personreach observed in synthetic panel pwgt is the person weight of the givenpanelist pct is the total number of times this panelist viewed thisstation(s) during this time period Person-level Repertoire - If cellneeds to be increased: corresponding to the penalty = 1 − view_pctlikelihood that the If cell needs to be decreased: panelist would beexposed penalty = view_pct to this type of programing Where: 1. Genreview_pct is the proportion of the 2. Station given panelist's tuningthat is spent watching a given genre or station Person-level Activity -If cell needs to be increased: corresponding to the penalty = 1 −view_pct likelihood that the If cell needs to decreased: panelist wouldbe exposed penalty = view_pct to media at this time of day Where: 1.Quarter Hour* view_pct is the proportion of the 2. Daypart reportingperiod that a panelist is watching any station during a given quarterhour* or daypart *Note that to properly capture the differences betweenpanelist behavior on weekdays vs weekends, the proportion spent viewinga given quarter hour is broken out by day of week. A panelist might viewthe 92^(nd) quarter hour 10% of the time on Mondays but 80% of the timeon Saturdays.

The example penalty determiner 306 of FIG. 3 determines the finalpenalty corresponding to a person (e.g., seed panelist) and/or household(e.g., seed panelist household) based on a weighted sum of thepenalties. In some examples, the penalty determiner 306 adjusts theweights based on adjustments made to the seed panelists.

The example seed panelist data adjuster 308 of FIG. 3 adjusts the seedpanel to satisfy the various targets and/or constraints. The exampleseed panelist data adjuster 308 adjusts the seed panel by adding and/orsubtracting quarter hours for a selected panelist (e.g., a seed panelistthat may be adjusted). The example seed panelist data adjuster 308adds/subtracts quarter hours by incrementing or decrementing the rating(e.g., the current seed panel rating) for the demographic of the seedpanelists by the weight of the panelist. In this manner, the exampleseed panelist data adjuster 308 uses the new, updated ratings total inall subsequent calculations.

The example household data adjuster 310 of FIG. 3 adjusts seed panelhouseholds to satisfy the various targets and/or constraints. Theexample household data adjuster 310 adjusts a household by adding and/orsubtracting quarter hours for a household (e.g., a household that may beadjusted). The household data adjuster 310 adds a quarter hour to ahousehold by determining how many seed panelists in the household haveviewing to the given station, program, etc. during the given quarterhour. If the count is zero, the example household data adjuster 310increments the household rating by the household weight. If the count isgreater than zero, the household data adjuster 310 does not change thehousehold rating, but the household count is increased by one. Thehousehold data adjuster 310 subtracts a quarter hour to a household in asimilar manner by decrementing the household rating and/or householdcount based on the count.

The example file generator 312 of FIG. 3 generates the example outputfile 130 based on the adjusted seed panel corresponding to the targetrating(s) and/or reach(es). As described above, the example filegenerator 312 generates the example output file 130 to include syntheticrespondent level data, synthetic respondent level attributes, quarterhour ratings calculated from the synthetic respondent level data,daypart ratings calculated from the synthetic respondent level data,daypart reach calculated from the synthetic respondent level data, etc.The example file generator 312 may output the example output file 130 toan additional device for further processing and/or to generate a report.

While an example manner of implementing the example seed panel generator122 of FIG. 1 is illustrated in FIG. 2 and/or the example seed paneloptimizer 128 of FIG. 1 is illustrated in FIG. 3, one or more elements,processes and/or devices illustrated in FIGS. 2 and/or 3 may becombined, divided, re-arranged, omitted, eliminated and/or implementedin any other way. Further, the example metering receiver 202, theexample tuning session determiner 204, the example presentation sessiondeterminer 206, the example modeler 208, the example model storage 210,the example tuning data receiver 212, the example duration determiner214, the example presentation session estimator 216, the examplereporter 218, and/or, more generally, the example seed panel generator122, of FIG. 2 and/or the example interface(s) 300, the exampleconstraint determiner 302, the example rating/reach comparer 304, theexample penalty determiner 306, the example seed panelist data adjuster308, the example household data adjuster 310, the example file generator312, and/or, more generally, the example seed panel optimizer 128 may beimplemented by hardware, machine readable instructions, software,firmware and/or any combination of hardware, machine readableinstructions, software and/or firmware. Thus, for example, any of theexample metering receiver 202, the example tuning session determiner204, the example presentation session determiner 206, the examplemodeler 208, the example model storage 210, the example tuning datareceiver 212, the example duration determiner 214, the examplepresentation session estimator 216, the example reporter 218, and/or,more generally, the example the example seed panel generator 122, ofFIG. 2 and/or the example interface(s) 300, the example constraintdeterminer 302, the example rating/reach comparer 304, the examplepenalty determiner 306, the example seed panelist data adjuster 308, theexample household data adjuster 310, the example file generator 312,and/or, more generally, the example seed panel optimizer 128 could beimplemented by one or more analog or digital circuit(s), logiccircuit(s), programmable processor(s), application specific integratedcircuit(s) (ASIC(s)), programmable logic device(s) (PLD(s)) and/or fieldprogrammable logic device(s) (FPLD(s)). When reading any of theapparatus or system claims of this patent to cover a purely softwareand/or firmware implementation, at least one the example meteringreceiver 202, the example tuning session determiner 204, the examplepresentation session determiner 206, the example modeler 208, theexample model storage 210, the example tuning data receiver 212, theexample duration determiner 214, the example presentation sessionestimator 216, the example reporter 218, and/or, more generally, theexample the example seed panel generator 122, of FIG. 2 and/or theexample interface(s) 300, the example constraint determiner 302, theexample rating/reach comparer 304, the example penalty determiner 306,the example seed panelist data adjuster 308, the example household dataadjuster 310, the example file generator 312, and/or, more generally,the example seed panel optimizer 128 is/are hereby expressly defined toinclude a tangible computer readable storage device or storage disk suchas a memory, a digital versatile disk (DVD), a compact disk (CD), aBlu-ray disk, etc. storing the software and/or firmware. Further still,the example seed panel generator 122 of FIG. 2 and/or the example seedpanel optimizer 128 of FIG. 3 may include one or more elements,processes and/or devices in addition to, or instead of, thoseillustrated in FIGS. 2 and 3, and/or may include more than one of any orall of the illustrated elements, processes and devices.

Flowcharts representative of example machine readable instructions forimplementing the example seed panel generator 122 of FIG. 2 are shown inFIGS. 4-6 and example machine readable instructions for implementing theexample seed panel optimizer 128 of FIG. 3 are shown in FIG. 7-11. Inthe examples, the machine readable instructions comprise a program forexecution by a processor such as the processor 1212, 1312 shown in theexample processor platform 1200, 1300 discussed below in connection withFIGS. 12 and/or 13. The program may be embodied in software stored on atangible computer readable storage medium such as a CD-ROM, a floppydisk, a hard drive, a digital versatile disk (DVD), a Blu-ray disk, or amemory associated with the processor 1212, 1312 but the entire programand/or parts thereof could alternatively be executed by a device otherthan the processor 1212, 1312 and/or embodied in firmware or dedicatedhardware. Further, although the example program is described withreference to the flowcharts illustrated in FIGS. 4-11, many othermethods of implementing the example seed panel generator 122 of FIG. 2and/or the example seed panel optimizer 128 of FIG. 3 may alternativelybe used. For example, the order of execution of the blocks may bechanged, and/or some of the blocks described may be changed, eliminated,or combined.

As mentioned above, the example processes of FIGS. 4-11 may beimplemented using coded instructions (e.g., computer and/or machinereadable instructions) stored on a tangible computer readable storagemedium such as a hard disk drive, a flash memory, a read-only memory(ROM), a compact disk (CD), a digital versatile disk (DVD), a cache, arandom-access memory (RAM) and/or any other storage device or storagedisk in which information is stored for any period (e.g., for extendedtime periods, permanently, for brief instances, for temporarilybuffering, and/or for caching of the information). As used herein, theterm tangible computer readable storage medium is expressly defined toinclude any type of computer readable storage device and/or storage diskand to exclude propagating signals and to exclude transmission media. Asused herein, “tangible computer readable storage medium” and “tangiblemachine readable storage medium” are used interchangeably. Additionallyor alternatively, the example processes of FIGS. 4-11 may be implementedusing coded instructions (e.g., computer and/or machine readableinstructions) stored on a non-transitory computer and/or machinereadable medium such as a hard disk drive, a flash memory, a read-onlymemory, a compact disk, a digital versatile disk, a cache, arandom-access memory and/or any other storage device or storage disk inwhich information is stored for any period (e.g., for extended timeperiods, permanently, for brief instances, for temporarily buffering,and/or for caching of the information). As used herein, the termnon-transitory computer readable medium is expressly defined to includeany type of computer readable storage device and/or storage disk and toexclude propagating signals and to exclude transmission media. As usedherein, when the phrase “at least” is used as the transition term in apreamble of a claim, it is open-ended in the same manner as the term“comprising” is open ended.

FIG. 4 is an example flowchart 400 representative of example machinereadable instructions that may be executed by the example seed panelgenerator 122 of FIGS. 1 and 2 to generate a seed panel. Although theinstructions of FIG. 4 are described in conjunction with the exampleseed panel generator 122 of FIGS. 1 and 2, the example instructions maybe utilized by any type of seed panel generator.

At block 402, the example interface(s) 200 receive modelled users (e.g.,corresponding to the audience assigned return path data) and/ormonitored panelists (e.g., users corresponding to the example meter data102). At block 404, the example data assigner 202 assigns geography andincome to the modeled return path data users and the monitoredpanelists. As described above in conjunction with FIG. 2, the exampledata assigner 202 assigns geography and income based on designatedmarked area definitions by county, universe estimates for each county(e.g., number of homes, demographic composition of the homes, etc.),and/or demographics of seed panel homes to assign the geography data.

At block 406, the example station translator 204 receives station datacorresponding to counties of the monitored panelists' locations from theexample station data storage 124. At block 408, the example stationtranslator 204 translates viewing of local stations. The example stationtranslator 204 translates the viewing based on a receivability list forthe counties corresponding to the monitored panelists, as describedbelow in conjunction with FIG. 5. At block 410, the example dataassigner 202 selects monitored panelists from the example paneliststorage 120 to represent the data assigned modelled users (e.g., returnpath data audience) to initiate the seed panel. At block 412, theexample attributes filter 206 filters out unwanted attributes, leavingthe desired attributes remaining. As described above in conjunction withFIG. 2, such desired attributes may be at the person level and/or thehousehold level, including, but not limited to, age, gender, ethnicity,nationality, race, head of household data, cable data, single set data,ADS data, county data, metro data, income, zip code, number oftelevisions, pay service data, etc.

At block 414, the example weighter 208 weights the seed panel based on auniverse estimate. The universe estimate is an estimate of the totalnumber of users in a universe of users. In some examples, the universeestimate is broken down at the demographic level. At block 416, theexample viewing donator 210 determines if there is an out-of-tab seedpanelist in the seed panel. As described above, a seed panelist isout-of-tab when the example return path data 100 and/or the examplemeter data 102 is faulty (e.g., includes an error) and/or is not beingtransmitted to the example AME 114. If the example viewing donator 210determines that there is an out-of-tab seed panelist in the seed panel(block 416: YES), the example viewing donator 210 donates viewing to theout-of-tab seed panelist. The process for donating viewing is furtherdescribed in conjunction with FIG. 6. At block 420, the example seedpanelist replicator 212 replicates and/or down-samples seed panelists toone or more of the seed panelists to finalize the seed panel. Asdescribed above in conjunction with FIG. 2, the example seed panelistreplicator 212 replicates and/or down-samples seed panelist to increaseand/or decrease the degrees of freedom of the example seed panel. Atblock 422, the example interface(s) 200 transmits the final seed panelto the example seed panel storage 126.

FIG. 5 is an example flowchart 408 representative of example machinereadable instructions that may be executed to implement the example seedpanel generator 122 of FIGS. 1 and 2 to translate viewing of localstations, as described above in conjunction with block 408 of FIG. 4.Although the instructions of FIG. 5 are described in conjunction withthe example seed panel generator 122 of FIGS. 1 and 2, the exampleinstructions may be utilized by any type of seed panel generator.

At block 500, the example station translator 204 generates a group ofseed panelists corresponding to the example meter data 102. At block502, the example station translator 204 identifies counties of each seedpanelist in the group. The counties are identified based on locationdata corresponding to the seed panelists stored in the example panelistdata storage 120 and/or any other storage or database. At block 504, theexample station translator 204 selects a county from the identifiedcounties.

At block 506, the example station translator 204 determines if theselected county is out-of-market. The example station translator 204 maydetermine that the selected county is out-of-market when the meter data102 of seed panelists in the selected county corresponds to stationswhich are not included in the seed panelists station receivability. Ifthe example station translator 204 determines that the selected countyis out-of-market (block 506: YES), the example station translator 204compares the center of the selected county to a television servicecontour (e.g., stored in the example station data database 124 ofFIG. 1) to identify stations corresponding to the selected county (block508). In some examples, the station translator 204 determines the centerof the selected county to be the location representative of the centerof the population of the county. All stations corresponding to thecounty point are considered receivable for over the air only homeswithin the county.

If the example station translator 204 determines that the selectedcounty is out-of-market (block 506: NO), the example station translator204 determines the number of non-receivable affiliates(s) thatcorrespond to the non-receivable station (block 510). If the examplestation translator 204 determines that there are not any receivableaffiliates corresponding to the non-receivable station (block 510: 0),the example station translator 204 removes tuning and/or viewing datafrom the seed panelist(s) corresponding to the selected county (block512). If the example station translator 204 determines that there is onereceivable affiliate corresponding to the non-receivable station (block510: 1), the example station translator 204 ascribes tuning and/orviewing data to the local affiliate for seed panelists corresponding tothe selected country (block 514). If the example station translator 204determines that there are more than one receivable affiliatecorresponding to the non-receivable station (block 510: >1), the examplestation translator 204 sums the tuning minutes for each receivablestation (block 516).

At block 518, the example station translator 204 normalizes the sum togenerate an affiliate probability. At block 520, the example stationtranslator 204 converts tuning and/or viewing data of the households inthe selected county to a candidate receivability session based on theaffiliates. The example station translator 204 selects the candidatereceivability session based on the affiliate probability. At block 522,the example station translator 204 determines if there are additionalcounties to process. If the example station translator 204 determinesthat there are additional counties to process (block 522: YES), theexample station translator 204 returns to block 504 to translate viewingfor the additional counties. If the example station translator 204determines that there are not additional counties to process (block 522:NO), the process ends.

FIG. 6 is an example flowchart 418 representative of example machinereadable instructions that may be executed to implement the example seedpanel generator 122 of FIGS. 1 and 2 to donate viewing, as describedabove in conjunction with block 418 of FIG. 4. Although the instructionsof FIG. 6 are described in conjunction with the example seed panelgenerator 122 of FIGS. 1 and 2, the example instructions may be utilizedby any type of seed panel generator.

At block 600, the example viewing donator 210 generates a group ofout-of-tab seed panelists of the seed panel. At block 602, the exampleviewing donator 210 selects a first out-of-tab seed panelist from thegenerated group. At block 604, the example viewing donator 210determines if the donor pool associated with the selected out-of-tabseed panelist is empty. As described above in conjunction with FIG. 2,the donor pool is a pool of seed panelists and/or monitored paneliststhat have similar demographic data as the selected out-of-tab seedpanelist. If the example viewing donator 210 determines that the donorpool associated with the selected panelist is not empty (block 604: NO),the example viewing donator 210 randomly selects a donor from the donorpool. Additionally or alternatively, the example viewing donator 210 mayselect a donor that most closely matches the demographics and/or viewinghabits of the selected panelist. At block 608, the example viewingdonator 210 uses the selected donor's viewing for the selected panelist.In this manner, the lack of viewing data from the out-of-tab seedpanelist may be supplemented to increase the accuracy of the seed panel.

If the example viewing donator 210 determines that the donor poolassociated with the selected panelist is empty (block 606: YES), theexample viewing donator 210 sets the selected panelist viewing to noviewing. At block 612, the example viewing donator 210 determines ifthere is an additional seed panelist in the out-of-tab seed panelistgroup. If the example viewing donator 210 determines that there is anadditional seed panelist in the out-of-tab seed panelist group (block612: YES), the example viewing donator 210 repeats this process for theadditional out-of-tab seed panelist in the group.

FIG. 7 is an example flowchart 700 representative of example machinereadable instructions that may be executed by the example seed paneloptimizer 128 of FIGS. 1 and 2 to adjust the seed panel based on targetratings/reach. Although the instructions of FIG. 7 are described inconjunction with the example seed panel optimizer 128 of FIGS. 1 and 2,the example instructions may be utilized by any type of seed paneloptimizer.

At block 702, the example constraint determiner 302 defines the audienceconstraints. At block 704, the example constraint determiner defines thereach constraints. The audience constraint(s) and/or reach constraint(s)may be included in a quarter hour ratings file and may be expanded to aday part, a day, a week, a month, etc., as described above inconjunction with FIG. 3. At block 706, the example seed panel optimizer128 optimizes the seed panel based on the current rating of the seedpanel and a received target rating. For example, if the received targetrating is 5% of African American users that watched a program at a firsttime and the current rating corresponds to 3% of African American usersthat watched the program at the first time, the seed panel optimizer 128optimizes the seed panel to model the target rating more closely. Theoptimization process is further described in conjunction with FIG. 8.

At block 708, the example seed panelist data adjuster 308 determinesviewership error of the adjusted seed panel. The example seed panelistdata adjuster determines the viewership error based on a differencebetween the current viewership and the target viewership for aparticular cell (e.g., a demographic and time corresponding to thetarget rating). At block 710, the example seed panelist data adjuster308 determines if the viewership error satisfies a first threshold (e.g.a viewing threshold). For example, if the viewership error is greaterthan the first threshold (e.g., the viewership is overstated where thecurrent viewership is higher than the target viewership), the seedpanelist data adjuster 308 determines that the viewership error does notsatisfy the first threshold. If the example seed panelist data adjuster308 determines that the viewership error does not satisfy the firstthreshold (block 710: NO), the example seed panel optimizer 128decrements the viewership until the first threshold is satisfied or theviewership can no longer be decremented (block 712), as described belowin conjunction with FIG. 9.

If the example seed panelist data adjuster 308 determines that theviewership error does satisfy the first threshold (block 710: YES), theexample seed panelist data adjuster 308 determines if the viewershiperror satisfies a second threshold (e.g. −1×(the viewing threshold))(block 714). For example, if the viewership error is less than thesecond threshold (e.g., the viewership is understated where the currentviewership is lower than the target viewership), the seed panelist dataadjuster 308 determines that the viewership error does not satisfy thesecond threshold. If the example seed panelist data adjuster 308determines that the viewership error does not satisfy the secondthreshold (block 714: NO), the example seed panel optimizer 128increments the viewership until the first threshold is satisfied or theviewership can no longer be incremented (block 716), as described belowin conjunction with FIG. 10.

At block 718, the example seed panelist data adjuster 308 determines ifall quarter-hour rating constraints are satisfied. If all thequarter-hour rating constraints are not satisfied (block 718: NO), theprocess returns to block 708, and the viewership adjustment loop isrepeated until all quarter hour ratings constraints are met. In someexamples, the process is repeated until all quarter hour ratingconstraints are met within some tolerance level. If the example seedpanelist data adjuster 308 determines that all quarter hour ratingconstraints have been satisfied (or have meet the tolerance level)(block 718: YES), the example household data adjuster generates tuningwithout viewing data (block 720), as further described in conjunctionwith FIG. 11. At block 722, the example file generator 312 generates theexample output file 130 including synthetic respondent level data forthe adjusted seed panel.

FIG. 8 is an example flowchart 706 representative of example machinereadable instructions that may be executed to implement the example seedpanel optimizer 128 of FIGS. 1 and 3 to optimize the seed panel based onthe current rating and the target rating, as described above inconjunction with block 706 of FIG. 7. Although the instructions of FIG.8 are described in conjunction with the example seed panel optimizer 128of FIGS. 1 and 3, the example instructions may be utilized by any typeof seed panel optimizer.

At block 800, the example ratings/reach comparer 304 compares the targetratings to the current ratings, the target household rating to thecurrent household rating, and the target reach to the current reach ofthe seed panel. For example, the rating/reach comparer 304 compares thetarget ratings to the current ratings by calculating a differencebetween the target ratings and the current ratings (e.g., the ratingdifference). The example rating/reach comparer 304 may compare thetarget household rating to the current household rating, and the targetreach to the current reach in a similar manner to determine a householdrating difference and/or a reach difference.

At block 802, the example rating/reach comparer 304 determines if therating difference (e.g., the difference between the target rating andthe current rating) and/or the household rating (e.g., the differencebetween the target household rating and the current household rating)satisfies a threshold (e.g., a viewing threshold). In some examples, theratings/reach comparer 304 analyzes the rating difference and thehousehold rating difference. In some examples, the ratings/reachcomparer 304 analyzes one of the rating difference or the householdrating difference. If the example ratings/reach comparer 304 determinesthat the rating difference and/or the household rating differencesatisfies the threshold (block 802: YES), the process ends. If theexample ratings/reach comparer 304 determines that the rating differenceand/or the household rating difference does not satisfy the threshold(block 802: NO), the example seed panelist data adjuster 308 generates asubgroup of seed panelists whose quarter hours may be adjusted (block804). For example, to decrease quarter hours of a selected panelist of agiven station and/or quarter hour, the selected panelist must have tunedinto the station during the quarter hour. Additionally, to increase aquarter hour of a selected panelist of a given station and/or quarterhour, the selected panelist must (A) be capable of receiving the media,(B) have less than a maximum number of tuning events during any quarterhour associated with the media, and (C) the selected panelist's weightmust be less than two times the rating balance for them to be includedin the subgroup.

At block 806, the example seed panelist data adjuster 308 determines theeffect on the rating difference and/or the household rating differencecaused by adjusting the quarter hours of each seed panelist in thesubgroup. In some examples, the seed panelist data adjuster 308determines the effect of each seed panelist to identify which seedpanelist adjustment (e.g. increase or decrease of quarter hour(s) foreach seed panelist) will contribute the most and/or least to the ratingsdifference and/or the household ratings difference. At block 808, theexample seed panelist data adjuster 308 selects the seed panelist fromthe subgroup that corresponds to the largest effect on the ratingdifference and/or household rating difference (e.g., the adjustmentthat, when applied, causes the current rating to be closer to the targetrating and/or causes the current household rating to be closer to thetarget household rating).

At block 810, the example seed panelist data adjuster 308 adjusts thequarter hour(s) of the selected seed panelist. As described above inconjunction with FIG. 3, the example seed panelist data adjuster 308adjusts the quarter hour(s) of the selected seed panelist byincrementing and/or decrementing the weight of the selected seedpanelist in the current ratings for the seed panelist's demographic. Atblock 812, the example seed panelist data adjuster 308 recalculates thecurrent ratings (e.g., person ratings and/or household ratings) andreach (e.g., person and/or household reach) based on the adjusted seedpanelist. In this manner, subsequent adjustment and/or calculations willbe based on the recalculated current ratings and/or reach. At block 814,the example seed panelist data adjuster 308 removes theselected/adjusted seed panelist from the subgroup.

At block 816, the example ratings/reach comparer 304 determines if therating difference and/or the household rating difference satisfies thethreshold. If the example ratings/reach comparer 304 determines that therating difference and/or the household rating difference satisfies thethreshold (block 816: YES), the process ends. The example ratings/reachcomparer 304 determines that the rating difference and/or the householdrating difference does not satisfy the threshold (block 816: NO), theexample seed panelist data adjuster 308 determines if the subgroup ofseed panelists is empty (e.g., each seed panelist's quarter hours wereadjusted and each seed panelist was removed from the subgroup) (block818). If the example seed panelist data adjuster 308 determines that thesubgroup of seed panelists is not empty (block 818: NO), the processreturns to block 806 to further adjust quarter hours of remaining seedpanelists in the subgroup.

FIG. 9 is an example flowchart 712 representative of example machinereadable instructions that may be executed to implement the example seedpanel optimizer 128 of FIGS. 1 and 3 to decrement viewership, asdescribed above in conjunction with block 712 of FIG. 7. Although theinstructions of FIG. 9 are described in conjunction with the exampleseed panel optimizer 128 of FIGS. 1 and 3, the example instructions maybe utilized by any type of seed panel optimizer.

At block 900, the example seed panelist data adjuster 308 determines adecrement candidate pool from the adjusted seed panel. In some examples,the seed panelist data adjuster 308 includes adjusted seed panelists inthe decrement candidate pool when the adjusted seed panelists satisfyvarious conditions. Such conditions may include seed panelistscorresponding to the demographic of the target rating and/or seedpanelists corresponding to the audience of the station and quarter hourof the target rating.

At block 902, the example penalty determiner 306 determines the personsrating penalty, household ratings penalty, the persons reach penalty,and the household reach penalty for each seed panelist in the decrementcandidate pool. The determination of each penalty is described above inconjunction with Table 1. At block 904, the example penalty determiner306 determines the final penalty based on the weighted sum (e.g.,weighted by a coefficient) of the rating penalty, the household penalty,the persons reach penalty, and the household reach penalty for each seedpanelist in the decrement candidate pool. As described above inconjunction with block 812 of FIG. 8, the coefficient of an adjustedseed panelist may be adjusted, thereby affecting the weighted sum of thefinal penalty.

At block 906, the example seed panelist data adjuster 308 selects thepanelist with the smallest final penalty. At block 908, the example seedpanelist data adjuster 308 removes the selected seed panelist from thecell audience (e.g., the audience corresponding to the target rating)for station and quarter hour, thereby reducing the viewership error. Atblock 910, the example seed panelist data adjuster 308 recalculates thecurrent viewership. At block 912, the example seed panelist dataadjuster 308 removes the seed panelist from the decrement candidatepool. At block 914, the example ratings/reach comparer 304 determines,based on the recalculated current viewership, if the viewership errorsatisfies the first threshold (e.g., the viewing threshold).

If the example rating/reach comparer 304 determines that the viewershiperror satisfies the first threshold (block 914: YES), the process ends.If the example rating/reach comparer 304 determines that the viewershiperror does not satisfy the first threshold (block 914: NO), the exampleseed panelist data adjuster 308 determines if the decrement candidatepool is empty (block 916). If the example seed panelist data adjuster308 determines that the decrement candidate pool is empty (block 916:YES), the process ends. If the example seed panelist data adjuster 308determines that the decrement candidate pool is not empty (block 916:NO), the process returns to block 906 until the viewership error issufficiently reduced or the decrement candidate pool is empty.

FIG. 10 is an example flowchart 716 representative of example machinereadable instructions that may be executed to implement the example seedpanel optimizer 128 of FIGS. 1 and 3 to increment viewership, asdescribed above in conjunction with block 716 of FIG. 7. Although theinstructions of FIG. 10 are described in conjunction with the exampleseed panel optimizer 128 of FIGS. 1 and 3, the example instructions maybe utilized by any type of seed panel optimizer.

At block 1000, the example seed panelist data adjuster 308 determines anincrement candidate pool from the adjusted seed panel. In some examples,the seed panelist data adjuster 308 includes adjusted seed panelists inthe increment candidate pool when the adjusted seed panelists satisfyvarious conditions. Such conditions may include seed panelistscorresponding to the demographic of the target rating, seed panelistsnot corresponding to the audience of the station and quarter hour of thetarget rating, seed panelists that have viewed less than a maximumnumber of stations in the quarter hour corresponding to the targetrating, and/or seed panelists receivability includes the stationcorresponding to the target rating.

At block 1002, the example penalty determiner 306 determines the personsrating penalty, the household ratings penalty, the persons reachpenalty, and the household reach penalty for each seed panelist in theincrement candidate pool. The determinations of each penalty aredescribed above in conjunction with Table 1. At block 1004, the examplepenalty determiner 306 determines the final penalty based on theweighted sum (e.g., weighted based on a coefficient) of the ratingpenalty, the household penalty, the persons reach penalty, and thehousehold reach penalty for each seed panelist in the incrementcandidate pool. As described above in conjunction with FIG. 2, thecoefficient for each penalty may be adjusted by a user at runtime,thereby affecting the weighted sum of the final penalty score.

At block 1006, the example seed panelist data adjuster 308 selects thepanelist with the smallest final penalty. At block 1008, the exampleseed panelist data adjuster 308 adds the selected seed panelist to thecell audience (e.g., the audience corresponding to the target rating)for station and quarter hour, thereby reducing the viewership error. Atblock 1010, the example seed panelist data adjuster 308 recalculates thecurrent viewership. At block 1012, the example seed panelist dataadjuster 308 removes the seed panelist from the increment candidatepool. At block 1014, the example ratings/reach comparer 304 determines,based on the recalculated current viewership, if the viewership errorsatisfies the first threshold (e.g., the viewing threshold).

If the example rating/reach comparer 304 determines that the viewershiperror satisfies the first threshold (block 1014: YES), the process ends.If the example rating/reach comparer 304 determines that the viewershiperror does not satisfy the first threshold (block 1014: NO), the exampleseed panelist data adjuster 308 determines if the increment candidatepool is empty (block 1016). If the example seed panelist data adjuster308 determines that the increment candidate pool is empty (block 1016:YES), the process ends. If the example seed panelist data adjuster 308determines that the increment candidate pool is not empty (block 1016:NO), the process returns to block 1006 until the viewership error issufficiently reduced or the increment candidate pool is empty.

FIG. 11 is an example flowchart 720 representative of example machinereadable instructions that may be executed to implement the example seedpanel optimizer 128 of FIGS. 1 and 3 to generate tuning without viewingdata, as described above in conjunction with block 720 of FIG. 7.Although the instructions of FIG. 11 are described in conjunction withthe example seed panel optimizer 128 of FIGS. 1 and 3, the exampleinstructions may be utilized by any type of seed panel optimizer.

At block 1100, the example rating/reach comparer 304 determines iftuning without viewing quarter hours are below the viewing threshold.Tuning without viewing is added to accurately reflect household ratingtargets in the final synthetic respondent level data in the exampleoutput file 130 by accounting for tuning without viewing situations. Ifthe example ratings/reach comparer 304 determines that tuning withoutviewing quarter hours are below the viewing threshold (block 1100: YES),the example rating/reach comparer 304 determines that tuning withoutviewing does not need to be added to the respondent level data and theprocess ends.

If the example ratings/reach comparer 304 determines that tuning withoutviewing quarter hours are not below the viewing threshold (block 1100:NO), the example penalty determiner 306 determines the householdpenalties for each household in the synthetic respondent level data(e.g., corresponding to the households of the adjusted seed panel)(block 1102), as shown above in Table 1. At block 1104, the examplehousehold data adjuster 310 selects a household of the households in thesynthetic respondent level data with the lowest penalty. At block 1106,the example household data adjuster 310 adds quarter hour(s) to theselected household without associating with persons of the household, asdescribed above in conjunction with FIG. 3. At block 1108, the examplehousehold data adjuster 310 removes the selected household from thesynthetic respondent level data.

At block 1110, the example ratings/reach comparer 304 determines iftuning without viewing quarter hours are below the viewing threshold. Ifthe example ratings/reach comparer 304 determines that tuning withoutviewing quarter hours are below the viewing threshold (block 1110: YES),the example rating/reach comparer 304 determines that the respondentlevel data does not require further adjusting and the process ends. Ifthe example ratings/reach comparer 304 determines that tuning withoutviewing quarter hours are not below the viewing threshold (block 1110:NO), the example household data adjuster 310 determines if there areadditional households in the synthetic respondent level data (block1112). If the example household data adjuster 310 determines that thereare not additional households in synthetic respondent level data (block1112: NO), the example process ends. If the example household dataadjuster 310 determines that there are additional households insynthetic respondent level data (block 1112: YES), the example processreturns to block 1104 to continue to adjust household quarter hoursuntil the tuning without viewing quarter hours are below the viewingthreshold and/or there are no more additional household in thesynthetics respondent level data.

FIG. 12 is a block diagram of an example processor platform 1200 capableof executing the instructions of FIGS. 3-5 to implement the example seedpanel generator 122 of FIG. 1. The processor platform 1200 can be, forexample, a server, a personal computer, a mobile device (e.g., a cellphone, a smart phone, a tablet such as an iPad™), a personal digitalassistant (PDA), an Internet appliance, or any other type of computingdevice.

The processor platform 1200 of the illustrated example includes aprocessor 1212. The processor 1212 of the illustrated example ishardware. For example, the processor 1212 can be implemented byintegrated circuits, logic circuits, microprocessors or controllers fromany desired family or manufacturer.

The processor 1212 of the illustrated example includes a local memory1213 (e.g., a cache). The example processor 1212 of FIG. 12 executes theinstructions of FIGS. 4-6 to the example interface(s) 200, the exampledata assigner 202, the example station translator 204, the exampleattributes filter 206, the example weighter 208, the example viewingdonator 210, and/or the example seed panelist replicator 212 toimplement the example seed panel generator 122 of FIG. 2. The processor1212 of the illustrated example is in communication with a main memoryincluding a volatile memory 1214 and a non-volatile memory 1216 via abus 1218. The volatile memory 1214 may be implemented by SynchronousDynamic Random Access Memory (SDRAM), Dynamic Random Access Memory(DRAM), RAMBUS Dynamic Random Access Memory (RDRAM) and/or any othertype of random access memory device. The non-volatile memory 1216 may beimplemented by flash memory and/or any other desired type of memorydevice. Access to the main memory 1214, 1216 is controlled by a memorycontroller.

The processor platform 1200 of the illustrated example also includes aninterface circuit 1220. The interface circuit 1220 may be implemented byany type of interface standard, such as an Ethernet interface, auniversal serial bus (USB), and/or a PCI express interface.

In the illustrated example, one or more input devices 1222 are connectedto the interface circuit 1220. The input device(s) 1222 permit(s) a userto enter data and commands into the processor 1212. The input device(s)can be implemented by, for example, a sensor, a microphone, a camera(still or video), a keyboard, a button, a mouse, a touchscreen, atrack-pad, a trackball, isopoint and/or a voice recognition system.

One or more output devices 1224 are also connected to the interfacecircuit 1220 of the illustrated example. The output devices 1224 can beimplemented, for example, by display devices (e.g., a light emittingdiode (LED), an organic light emitting diode (OLED), a liquid crystaldisplay, a cathode ray tube display (CRT), a touchscreen, a tactileoutput device, and/or speakers). The interface circuit 1220 of theillustrated example, thus, typically includes a graphics driver card, agraphics driver circuit or a graphics driver processor.

The interface circuit 1220 of the illustrated example also includes acommunication device such as a transmitter, a receiver, a transceiver, amodem and/or network interface card to facilitate exchange of data withexternal machines (e.g., computing devices of any kind) via a network1226 (e.g., an Ethernet connection, a digital subscriber line (DSL), atelephone line, coaxial cable, a cellular telephone system, etc.).

The processor platform 1200 of the illustrated example also includes oneor more mass storage devices 1228 for storing software and/or data.Examples of such mass storage devices 1228 include floppy disk drives,hard drive disks, compact disk drives, Blu-ray disk drives, RAIDsystems, and digital versatile disk (DVD) drives.

The coded instructions 1232 of FIGS. 4-6 may be stored in the massstorage device 1228, in the volatile memory 1214, in the non-volatilememory 1216, and/or on a removable tangible computer readable storagemedium such as a CD or DVD.

FIG. 13 is a block diagram of an example processor platform 1300 capableof executing the instructions of FIGS. 7-11 to implement the exampleseed panel generator 132 of FIG. 1. The processor platform 1300 can be,for example, a server, a personal computer, a mobile device (e.g., acell phone, a smart phone, a tablet such as an iPad™), a personaldigital assistant (PDA), an Internet appliance, or any other type ofcomputing device.

The processor platform 1300 of the illustrated example includes aprocessor 1312. The processor 1312 of the illustrated example ishardware. For example, the processor 1312 can be implemented byintegrated circuits, logic circuits, microprocessors or controllers fromany desired family or manufacturer.

The processor 1312 of the illustrated example includes a local memory1313 (e.g., a cache). The example processor 1312 of FIG. 13 executes theinstructions of FIGS. 7-11 to the example interface(s) 300, the exampleconstraint determiner 302, the example rating/reach comparer 304, theexample penalty determiner 306, the example seed panelist data adjuster308, the example household data adjuster 310, and/or the example filegenerator 312 to implement the example seed panel optimizer 128 of FIG.3. The processor 1312 of the illustrated example is in communicationwith a main memory including a volatile memory 1314 and a non-volatilememory 1316 via a bus 1318. The volatile memory 1314 may be implementedby Synchronous Dynamic Random Access Memory (SDRAM), Dynamic RandomAccess Memory (DRAM), RAMBUS Dynamic Random Access Memory (RDRAM) and/orany other type of random access memory device. The non-volatile memory1316 may be implemented by flash memory and/or any other desired type ofmemory device. Access to the main memory 1314, 1316 is controlled by amemory controller.

The processor platform 1300 of the illustrated example also includes aninterface circuit 1320. The interface circuit 1320 may be implemented byany type of interface standard, such as an Ethernet interface, auniversal serial bus (USB), and/or a PCI express interface.

In the illustrated example, one or more input devices 1322 are connectedto the interface circuit 1320. The input device(s) 1322 permit(s) a userto enter data and commands into the processor 1312. The input device(s)can be implemented by, for example, a sensor, a microphone, a camera(still or video), a keyboard, a button, a mouse, a touchscreen, atrack-pad, a trackball, isopoint and/or a voice recognition system.

One or more output devices 1324 are also connected to the interfacecircuit 1320 of the illustrated example. The output devices 1324 can beimplemented, for example, by display devices (e.g., a light emittingdiode (LED), an organic light emitting diode (OLED), a liquid crystaldisplay, a cathode ray tube display (CRT), a touchscreen, a tactileoutput device, and/or speakers). The interface circuit 1320 of theillustrated example, thus, typically includes a graphics driver card, agraphics driver circuit or a graphics driver processor.

The interface circuit 1320 of the illustrated example also includes acommunication device such as a transmitter, a receiver, a transceiver, amodem and/or network interface card to facilitate exchange of data withexternal machines (e.g., computing devices of any kind) via a network1326 (e.g., an Ethernet connection, a digital subscriber line (DSL), atelephone line, coaxial cable, a cellular telephone system, etc.).

The processor platform 1300 of the illustrated example also includes oneor more mass storage devices 1328 for storing software and/or data.Examples of such mass storage devices 1328 include floppy disk drives,hard drive disks, compact disk drives, Blu-ray disk drives, RAIDsystems, and digital versatile disk (DVD) drives.

The coded instructions 1332 of FIGS. 6-11 may be stored in the massstorage device 1328, in the volatile memory 1314, in the non-volatilememory 1316, and/or on a removable tangible computer readable storagemedium such as a CD or DVD.

From the foregoing, it should be appreciated that the above disclosedmethods, apparatus, and articles of manufacture generate syntheticrespondent level data. Example disclosed herein process the collectedand/or aggregated metering data for markets where a panel is maintainedand collect and/or aggregate return path data for markets where a panelis not maintained to generate a seed panel. Once a seed panel has beengenerated, examples disclosed herein adjusts the seed panel to satisfytarget ratings and/or target reach. Using examples disclosed herein,consistent respondent level data is generated that satisfy varioustargets, thereby providing more accurate universe estimations.

Although certain example methods, apparatus and articles of manufacturehave been described herein, the scope of coverage of this patent is notlimited thereto. On the contrary, this patent covers all methods,apparatus and articles of manufacture fairly falling within the scope ofthe appended claims either literally or under the doctrine ofequivalents.

What is claimed is:
 1. An apparatus comprising: a comparator to comparea target rating to a computed rating to determine a comparison result,the computed rating determined based on a seed panel, the seed panelincluding monitored panelists associated with return path data; a seedpanelist data adjuster to adjust the seed panel based on the comparisonresult to reduce an error between the target rating and the computedrating; and a household data adjuster to add tuning without viewing datato households of the adjusted seed panel, the tuning without viewingdata for a first one of the households to represent monitored datacorresponding to a media presentation device of the first one of thehouseholds being on while a media output device in communication withthe media presentation device is off.
 2. The apparatus of claim 1,wherein the household data adjuster is to generate the tuning withoutviewing data by, when the tuning without viewing data is below athreshold: selecting a first household of a first panelist in theadjusted seed panel that (i) affects a relative error between whenviewing of the first household is modified and when the viewing of thefirst household is not modified and (ii) affects the relative error lessthan a second household, and the first household included in syntheticrespondent level-data; and removing the first household from thesynthetic respondent level-data to increase the tuning without viewingdata.
 3. The apparatus of claim 1, wherein the seed panel data adjusteris to adjust the seed panel by: selecting a panelist from seed panelistsin the seed panel that affects the error between the target rating andthe computed rating; and adjusting a weight for the selected panelist.4. The apparatus of claim 1, wherein: the target rating is determinedbased on the return path data and corresponds to a first percentage ofpersons of a first demographic exposed to first media at a first time;and the computed rating corresponds to a second percentage of seedpanelists exposed to the first media at the first time.
 5. The apparatusof claim 1, further including a data assigner to select the monitoredpanelists to be in the seed panel based on demographics associated witha population associated with the return path data.
 6. The apparatus ofclaim 5, wherein the data assigner is to select a first group of themonitored panelists to be seed panelists based on a comparison of themonitored panelists and the population associated with the return pathdata, and further including a weighter to weight the seed panelistsbased on a universe estimate of media users, the universe estimateincluding the monitored panelists and the population.
 7. The apparatusof claim 1, further including a seed panelist replicator to at least oneof replicate or down-sample seed panelists.
 8. The apparatus of claim 1,wherein the seed panel data adjuster is to adjust the seed panel basedon a viewership error corresponding to the seed panel, the viewershiperror based on a constraint corresponding to the target rating.
 9. Theapparatus of claim 8, wherein the seed panelist data adjuster is toadjust the seed panel based on the viewership error corresponding to theseed panel by: when the viewership error does not satisfy a firstthreshold: selecting a first panelist from seed panelists in the seedpanel that (i) affects relative error between when viewing of the firstpanelist is modified and when the viewing of the first panelist is notmodified and (ii) affects the relative error less than a secondpanelist; and removing the first panelist from an audience correspondingto the target rating; and when the viewership error does not satisfy asecond threshold different than the first threshold: selecting a thirdpanelist from the seed panelists in the seed panel that (i) affects therelative error between when viewing of the second panelist is modifiedand when viewing of the second panelist is not modified and (ii) affectsthe relative error less than a fourth panelist; and adding the firstpanelist from the audience corresponding to the target rating.
 10. Anapparatus comprising: memory including computer readable instructions;and a processor to execute the instructions to at least: compare atarget rating to a computed rating to determine a comparison result, thecomputed rating determined based on a seed panel, the seed panelincluding monitored panelists associated with return path data; adjustthe seed panel based on the comparison result to reduce an error betweenthe target rating and the computed rating; and add tuning withoutviewing data to households of the adjusted seed panel, the tuningwithout viewing data for a first one of the households to representmonitored data corresponding to a media presentation device of the firstone of the households being on while a media output device incommunication with the media presentation device is off.
 11. Theapparatus of claim 10, wherein the processor to generate the tuningwithout viewing data by, when the tuning without viewing data is below athreshold: selecting a first household of a first panelist in theadjusted seed panel that (i) affects a relative error between whenviewing of the first household is modified and when the viewing of thefirst household is not modified and (ii) affects the relative error lessthan a second household, and the first household included in syntheticrespondent level-data; and removing the first household from thesynthetic respondent level-data to increase the tuning without viewingdata.
 12. The apparatus of claim 10, wherein the processor is to adjustthe seed panel by: selecting a panelist from seed panelists in the seedpanel that affects the error between the target rating and the computedrating; and adjusting a weight for the selected panelist.
 13. Theapparatus of claim 10, wherein: the target rating is determined based onthe return path data and corresponds to a first percentage of persons ofa first demographic exposed to first media at a first time; and thecomputed rating corresponds to a second percentage of seed panelistsexposed to the first media at the first time.
 14. An apparatuscomprising: means for comparing a target rating to a computed rating todetermine a comparison result, the computed rating determined based on aseed panel, the seed panel including monitored panelists associated withreturn path data; means for adjusting the seed panel based on thecomparison result to reduce an error between the target rating and thecomputed rating; and means for adding tuning without viewing data tohouseholds of the adjusted seed panel, the tuning without viewing datafor a first one of the households to represent monitored datacorresponding to a media presentation device of the first one of thehouseholds being on while a media output device in communication withthe media presentation device is off.
 15. The apparatus of claim 14,wherein the adding means is to generate the tuning without viewing databy, when the tuning without viewing data is below a threshold: selectinga first household of a first panelist in the adjusted seed panel that(i) affects a relative error between when viewing of the first householdis modified and when the viewing of the first household is not modifiedand (ii) affects the relative error less than a second household, andthe first household included in synthetic respondent level-data; andremoving the first household from the synthetic respondent level-data toincrease the tuning without viewing data.
 16. The apparatus of claim 14,wherein the means for adjusting is to adjust the seed panel by:selecting a panelist from seed panelists in the seed panel that affectsthe error between the target rating and the computed rating; andadjusting a weight for the selected panelist.
 17. The apparatus of claim14, wherein: the target rating is determined based on the return pathdata and corresponds to a first percentage of persons of a firstdemographic exposed to first media at a first time; and the computedrating corresponds to a second percentage of seed panelists exposed tothe first media at the first time.
 18. A tangible computer readablestorage medium comprising instructions which, when executed cause one ormore processors to at least: compare a target rating to a computedrating to determine a comparison result, the computed rating determinedbased on a seed panel, the seed panel including monitored panelistsassociated with return path data; adjust the seed panel based on thecomparison result to reduce an error between the target rating and thecomputed rating; and add tuning without viewing data to households ofthe adjusted seed panel, the tuning without viewing data for a first oneof the households to represent monitored data corresponding to a mediapresentation device of the first one of the households being on while amedia output device in communication with the media presentation deviceis off.
 19. The computer readable storage medium of claim 18, whereinthe instructions cause the one or more processors to generate the tuningwithout viewing data by, when the tuning without viewing data is below athreshold: selecting a first household of a first panelist in theadjusted seed panel that (i) affects a relative error between whenviewing of the first household is modified and when the viewing of thefirst household is not modified and (ii) affects the relative error lessthan a second household, and the first household included in syntheticrespondent level-data; and removing the first household from thesynthetic respondent level-data to increase the tuning without viewingdata.
 20. The computer readable storage medium of claim 18, wherein theinstructions cause the one or more processors to adjust the seed panelby: selecting a panelist from seed panelists in the seed panel thataffects the error between the target rating and the computed rating; andadjusting a weight for the selected panelist.
 21. The computer readablestorage medium of claim 18, wherein: the target rating is determinedbased on the return path data and corresponds to a first percentage ofpersons of a first demographic exposed to first media at a first time;and the computed rating corresponds to a second percentage of seedpanelists exposed to the first media at the first time.
 22. The computerreadable storage medium of claim 18, wherein the instructions cause theone or more processors to select the monitored panelists to be in theseed panel based on demographics associated with a population associatedwith the return path data.
 23. The computer readable storage medium ofclaim 22, wherein the instructions cause the one or more processors to:select a first group of the monitored panelists to be seed panelistsbased on a comparison of the monitored panelists and the populationassociated with the return path data; and weight the seed panelistsbased on a universe estimate of media users, the universe estimateincluding the monitored panelists and the population.
 24. The computerreadable storage medium of claim 18, wherein the instructions cause theone or more processors to at least one of replicate or down-sample seedpanelists.
 25. The computer readable storage medium of claim 18, whereinthe instructions cause the one or more processors to adjust the seedpanel based on a viewership error corresponding to the seed panel, theviewership error based on a constraint corresponding to the targetrating.
 26. The computer readable storage medium of claim 25, whereinthe instructions cause the one or more processors to adjust the seedpanel based on the viewership error corresponding to the seed panel by:when the viewership error does not satisfy a first threshold: selectinga first panelist from seed panelists in the seed panel that (i) affectsrelative error between when viewing of the first panelist is modifiedand when the viewing of the first panelist is not modified and (ii)affects the relative error less than a second panelist; and removing thefirst panelist from an audience corresponding to the target rating; andwhen the viewership error does not satisfy a second threshold differentthan the first threshold: selecting a third panelist from the seedpanelists in the seed panel that (i) affects the relative error betweenwhen viewing of the second panelist is modified and when viewing of thesecond panelist is not modified and (ii) affects the relative error lessthan a fourth panelist; and adding the first panelist from the audiencecorresponding to the target rating.